Search Results (1,010)

Drone lidar has the potential to provide detailed measurements of vertical forest structure throughout large areas, but a systematic evaluation of unsampled forest structure in comparison to independent reference data has not been performed. Here, we used ray tracing on a high-resolution voxel grid to quantify sampling variation in a temperate mountain forest in the southwest Czech Republic. We decoupled the impact of pulse density and scan-angle range on the likelihood of generating a return using spatially and temporally coincident TLS data. We show three ways that a return can fail to be generated in the presence of vegetation: first, voxels could be searched without producing a return, even when vegetation is present; second, voxels could be shadowed (occluded) by other material in the beam path, preventing a pulse from searching a given voxel; and third, some voxels were unsearched because no pulse was fired in that direction. We found that all three types existed, and that the proportion of each of them varied with pulse density and scan-angle range throughout the canopy height profile. Across the entire data set, 98.1% of voxels known to contain vegetation from a combination of coincident drone lidar and TLS data were searched by high-density drone lidar, and 81.8% of voxels that were occupied by vegetation generated at least one return. By decoupling the impacts of pulse density and scan angle range, we found that sampling completeness was more sensitive to pulse density than to scan-angle range. There are important differences in the causes of sampling variation that change with pulse density, scan-angle range, and canopy height. Our findings demonstrate the value of ray tracing to quantifying sampling completeness in drone lidar. Full article

Functionally graded materials (FGMs) are revolutionizing various industries with their customizable properties, a key advantage over traditional composites. The rise of voxel-based 3D printing has furthered the development of FGMs with complex microstructures. Despite these advances, current design methods for FGMs often use abstract mathematical functions with limited relevance to actual performance. Furthermore, conventional micromechanics models for the analysis of FGMs tend to oversimplify, leading to inaccuracies in effective property predictions. To address these fundamental deficiencies, this paper introduces new gradation functions for functionally graded beams (FGBs) based on bending strain energy density, coupled with a voxel-based design and analysis approach. For the first time, these new gradation functions directly relate to structural performance and have proven to be more effective than conventional ones in improving beam performance, particularly under complex bending moments influenced by various loading and boundary conditions. This study reveals the significant role of primary and secondary gradation indices in material composition and distribution, both along the beam axis and across sections. It identifies optimal combinations of these indices for enhanced FGB performance. This research not only fills gaps in FGB design and analysis but also opens possibilities for applying these concepts to other strain energy density types, like shearing and torsion, and to different structural components such as plates and shells. Full article

The digital documentation and analysis of cultural heritage increasingly rely on high-precision three-dimensional point cloud data, which often suffers from missing regions due to limitations in acquisition conditions, hindering subsequent analyses and applications. Point cloud completion techniques, by predicting and filling these missing regions, are vital for restoring the integrity of cultural heritage structures, enhancing restoration accuracy and efficiency. In this paper, for challenges in processing large-scale cultural heritage point clouds, particularly the slow processing speed and visualization impairments from uneven point density during completion, we propose a point cloud completion employing centroid-based voxel feature extraction, which significantly accelerates feature extraction for massive point clouds. Coupled with an efficient upsampling module, it achieves a uniform point distribution. Experimental results show that the proposed method matches SOTA performance in completion accuracy while surpassing in point density uniformity, demonstrating capability in handling larger-scale point cloud data, and accelerating the processing of voluminous point clouds. In general, the proposed method markedly enhances the efficiency and quality of large-scale point cloud completion, holding significant value for the digital preservation and restoration of cultural heritage. Full article

Background: Lately, radiomic studies featuring the development of a signature to use in prediction models in diagnosis or prognosis outcomes have been increasingly published. While the results are shown to be promising, these studies still have many pitfalls and limitations. One of the main issues of these studies is that radiomic features depend on how the images are preprocessed before their computation. Since, in widely known and used software for radiomic features calculation, it is possible to set these preprocessing parameters before the calculation of the radiomic feature, there are ongoing studies assessing the stability and repeatability of radiomic features to find the most suitable preprocessing parameters for every used imaging modality. Materials and Methods: We performed a comprehensive literature search using four electronic databases: PubMed, Cochrane Library, Embase, and Scopus. Mesh terms and free text were modeled in search strategies for databases. The inclusion criteria were studies where preprocessing parameters’ influence on feature values and model predictions was addressed. Records lacking information on image acquisition parameters were excluded, and any eligible studies with full-text versions were included in the review process, while conference proceedings and monographs were disregarded. We used the QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies 2) tool to investigate the risk of bias. We synthesized our data in a table divided by the imaging modalities subgroups. Results: After applying the inclusion and exclusion criteria, we selected 43 works. This review examines the impact of preprocessing parameters on the reproducibility and reliability of radiomic features extracted from multimodality imaging (CT, MRI, CBCT, and PET/CT). Standardized preprocessing is crucial for consistent radiomic feature extraction. Key preprocessing steps include voxel resampling, normalization, and discretization, which influence feature robustness and reproducibility. In total, 44% of the included works studied the effects of an isotropic voxel resampling, and most studies opted to employ a discretization strategy. From 2021, several studies started selecting the best set of preprocessing parameters based on models’ best performance. As for comparison metrics, ICC was the most used in MRI studies in 58% of the screened works. Conclusions: From our work, we highlighted the need to harmonize the use of preprocessing parameters and their values, especially in light of future studies of prospective studies, which are still lacking in the current literature. Full article

This study proposes a three-dimensional (3D) deformation estimation framework based on the integration of shape and texture information for real-scene 3D model matching, effectively addressing the issue of deformation assessment in large-scale geological landslide areas. By extracting and merging the texture and shape features of matched points, correspondences between points in multi-temporal real-scene 3D models are established, resolving the difficulties faced by existing methods in achieving robust and high-precision 3D point matching over landslide areas. To ensure the complete coverage of the geological disaster area while enhancing computational efficiency during deformation estimation, a voxel-based thinning method to generate interest points is proposed. The effectiveness of the proposed method is validated through tests on a dataset from the Lijie north hill geological landslide area in Gansu Province, China. Experimental results demonstrate that the proposed method significantly outperforms existing classic and advanced methods in terms of matching accuracy metrics, and the accuracy of our deformation estimates is close to the actual measurements obtained from GNSS stations, with an average error of only 2.2 cm. Full article

Zhonghua Gate Castle is on the tentative list for Chinese World Cultural Heritage. Due to long-term sunshine, rain erosion, and man-made damage, its surface appears to have different degrees of cracks and other diseases. This paper centers on Zhonghua Gate Castle; terrestrial laser scanning is used to obtain the exterior wall point cloud data. A crack detection method based on point cloud data curved surface reconstruction is proposed. It involves data preprocessing, crack detection, and the analysis of crack features. This method initially uses data preprocessing techniques to improve data quality. These techniques include removing ground points and super-voxel segmentation. Subsequently, local surface reconstruction was employed to address the issue of missing point cloud data within cracks and the Euclidean clustering algorithm was used for precise crack identification. The article provides a detailed analysis of the geometric characteristics of cracks. They involve the calculation of length, width, and area. The results of the experiment demonstrate that the method could successfully identify cracks and extract geometric features and has millimeter-level accuracy compared to actual crack sizes. Full article

The L-PBF process belongs to the most modern methods of manufacturing complex-shaped parts. It is used especially in the automotive, aviation industries, and in the consumer products industry as well. Numerical simulation in the powder sintering process is a means of optimizing time efficiency, accuracy and predicting future errors. It is one of the means to optimize the L-PBF process, which makes it possible to investigate the influence of individual parameters on additive manufacturing. This research makes it possible to predict the correct orientation of a part based on selected criteria, which are assigned a weighting factor in the form of parameters with which the simulation software Simufact Additive can work. Based on these, three possible orientations of the part were analysed with respect to the area of the supporting material, the volume of the supporting material, the number of voxels, and the building risk. Finally, the results of a simulation and the results of the tensile test were compared. From the results of the static tensile test, as well as from the results of the numerical simulation, it was found that better characteristics were achieved for the orientation of part no. 1 compared to orientation of part No. 3. Full article

Human action recognition has facilitated the development of artificial intelligence devices focusing on human activities and services. This technology has progressed by introducing 3D point clouds derived from depth cameras or radars. However, human behavior is intricate, and the involved point clouds are vast, disordered, and complicated, posing challenges to 3D action recognition. To solve these problems, we propose a Symmetric Fine-coarse Neural Network (SFCNet) that simultaneously analyzes human actions’ appearance and details. Firstly, the point cloud sequences are transformed and voxelized into structured 3D voxel sets. These sets are then augmented with an interval-frequency descriptor to generate 6D features capturing spatiotemporal dynamic information. By evaluating voxel space occupancy using thresholding, we can effectively identify the essential parts. After that, all the voxels with the 6D feature are directed to the global coarse stream, while the voxels within the key parts are routed to the local fine stream. These two streams extract global appearance features and critical body parts by utilizing symmetric PointNet++. Subsequently, attention feature fusion is employed to capture more discriminative motion patterns adaptively. Experiments conducted on public benchmark datasets NTU RGB+D 60 and NTU RGB+D 120 validate SFCNet’s effectiveness and superiority for 3D action recognition. Full article

In this analysis, we aimed to investigate the effect of COVID-19 disease on eating behavior. A total of 55 right-handed adults, <50 years of age, without overweight or obesity, from two cross-sectional studies were included. The first one enrolled subjects between September 2018 and December 2019 (non-COVID-19 group). The second one included subjects enrolled between March 2022 and May 2023; for this analysis, 28 with a history of COVID-19 (COVID-19 group) were retained. Hunger, TFEQ-18, plasma ghrelin, neuropeptide Y (NPY) and resting-state fMRI were assessed during fasting. Intraregional neuronal synchronicity and connectivity were assessed by voxel-based regional homogeneity (ReHo) and degree of centrality (DC). Significantly higher ghrelin and NPY levels were observed in the COVID-19 group than in the non-COVID-19 group (ghrelin 197.5 pg/mL vs. 67.1 pg/mL, p < 0.001; NPY 128.0 pg/mL vs. 84.5 pg/mL, p = 0.005). The NPY levels positively correlated with the DC and ReHo in the left lingual (r = 0.67785 and r = 0.73604, respectively). Similar scores were noted for cognitive restraint, uncontrolled eating and emotional eating in both groups according to the TFEQ-18 questionnaire results (p > 0.05 for all). Our data showed increased levels of appetite-related hormones, correlated with activity in brain regions involved in appetite regulation, persisting long after COVID-19 infection. Full article

Recent evidence suggests a connection between hyperacusis and the motor system of the brain. For instance, our recent study reported that hyperacusis in participants with tinnitus and hearing loss is associated with smaller gray matter volumes in the supplementary motor area (SMA). Given that hearing loss can affect gray matter changes in tinnitus, this study aimed to determine if the changes reported in our previous findings of smaller SMA gray matter volumes in hyperacusis persist in the absence of hearing loss. Data for this study were gathered from four prior studies conducted between 2004 and 2019 at the University Medical Centre Groningen (UMCG). A total of 101 participants with tinnitus and either clinically normal hearing (normal hearing with tinnitus or NHT, n = 35) or bilateral sensorineural hearing loss (hearing loss with tinnitus or HLT, n = 66) were included across four studies. Hyperacusis was determined by a score of ≥22 on the Hyperacusis Questionnaire (HQ). In the NHT group, 22 (63%) participants scored ≥22 on the HQ (NHT with hyperacusis: mean age 44.1 years, 12 females), while in the HLT group, 25 (38%) participants scored ≥22 on the HQ (HLT with hyperacusis: mean age 59.5 years, 10 females). The 2 × 2 between-group ANOVAs revealed that hyperacusis is associated with smaller SMA gray matter volumes, regardless of hearing levels. Notably, the smaller SMA gray matter volumes in hyperacusis were primarily influenced by the attentional subscales of the HQ. The association between hyperacusis and the motor system may indicate a constant alertness to sounds and a readiness for motor action. Full article

Recently, the growing demand for autonomous driving in the industry has led to a lot of interest in 3D object detection, resulting in many excellent 3D object detection algorithms. However, most 3D object detectors focus only on a single set of LiDAR points, ignoring their potential ability to improve performance by leveraging the information provided by the consecutive set of LIDAR points. In this paper, we propose a novel 3D object detection method called temporal motion-aware 3D object detection (TM3DOD), which utilizes temporal LiDAR data. In the proposed TM3DOD method, we aggregate LiDAR voxels over time and the current BEV features by generating motion features using consecutive BEV feature maps. First, we present the temporal voxel encoder (TVE), which generates voxel representations by capturing the temporal relationships among the point sets within a voxel. Next, we design a motion-aware feature aggregation network (MFANet), which aims to enhance the current BEV feature representation by quantifying the temporal variation between two consecutive BEV feature maps. By analyzing the differences and changes in the BEV feature maps over time, MFANet captures motion information and integrates it into the current feature representation, enabling more robust and accurate detection of 3D objects. Experimental evaluations on the nuScenes benchmark dataset demonstrate that the proposed TM3DOD method achieved significant improvements in 3D detection performance compared with the baseline methods. Additionally, our method achieved comparable performance to state-of-the-art approaches. Full article

Proper documentation is fundamental to providing structural health monitoring, damage identification and failure assessment for Cultural Heritage (CH). Three-dimensional models from photogrammetric and laser scanning surveys usually provide 3D point clouds that can be converted into meshes. The point clouds usually contain noise data due to different causes: non-cooperative material or surfaces, bad lighting, complex geometry and low accuracy of the instruments utilized. Point cloud denoising has become one of the hot topics of 3D geometric data processing, removing these noise data to recover the ground-truth point cloud and adding smoothing to the ideal surface. These cleaned point clouds can be converted in volumes with different algorithms, suitable for different uses, mainly for structural analysis. This paper aimed to analyse the geometric accuracy of algorithms available for the conversion of 3D point clouds into volumetric models that can be used for structural analyses through the FEA process. The process is evaluated, highlighting problems and difficulties that lie in poor reconstruction results of volumes from denoised point clouds due to the geometric complexity of the objects. Full article

Accurate and comprehensive 3D objects detection is important for perception systems in autonomous driving. Nevertheless, contemporary mainstream methods tend to perform more effectively on large objects in regions proximate to the LiDAR, leaving limited exploration of long-range objects and small objects. The divergent point pattern of LiDAR, which results in a reduction in point density as the distance increases, leads to a non-uniform point distribution that is ill-suited to discretized volumetric feature extraction. To address this challenge, we propose the Foreground Voxel Proposal (FVP) module, which effectively locates and generates voxels at the foreground of objects. The outputs are subsequently merged to mitigating the difference in point cloud density and completing the object shape. Furthermore, the susceptibility of small objects to occlusion results in the loss of feature space. To overcome this, we propose the Multi-Scale Feature Integration Network (MsFIN), which captures contextual information at different ranges. Subsequently, the outputs of these features are integrated through a cascade framework based on transformers in order to supplement the object features space. The extensive experimental results demonstrate that our network achieves remarkable results. Remarkably, our approach demonstrated an improvement of 8.56% AP on the SECOND baseline for the Car detection task at a distance of more than 20 m, and 9.38% AP on the Cyclist detection task. Full article

Background: Humans perceive and interpret the world through the lens of self-reference processes, typically facilitating enhanced performance for the task at hand. However, this research has predominantly emphasized the automatic facet of self-reference processing, overlooking how it interacts with control processes affecting everyday situations. Methods: We investigated this relationship between automatic and control self-reference processing in neuropsychological patients performing self-face perception tasks and the Birmingham frontal task measuring executive functions. Results: Principal component analysis across tasks revealed two components: one loaded on familiarity/orientation judgments reflecting automatic self-reference processing, and the other linked to the cross task and executive function indicating control processing requirements. Voxel-based morphometry and track-wise lesion-mapping analyses showed that impairments in automatic self-reference were associated with reduced grey matter in the ventromedial prefrontal cortex and right inferior temporal gyrus, and white matter damage in the right inferior fronto-occipital fasciculus. Deficits in executive control were linked to reduced grey matter in the bilateral inferior parietal lobule and left anterior insula, and white matter disconnections in the left superior longitudinal fasciculus and arcuate fasciculus. Conclusions: The causal evidence suggests that automatic and control facets of self-reference processes are subserved by distinct yet integrated ventral prefrontal–temporal and dorsal frontal–parietal networks, respectively. Full article

Background: Pancreatic cysts in autosomal dominant polycystic kidney disease (ADPKD) correlate with PKD2 mutations, which have a different phenotype than PKD1 mutations. However, pancreatic cysts are commonly overlooked by radiologists. Here, we automate the detection of pancreatic cysts on abdominal MRI in ADPKD. Methods: Eight nnU-Net-based segmentation models with 2D or 3D configuration and various loss functions were trained on positive-only or positive-and-negative datasets, comprising axial and coronal T2-weighted MR images from 254 scans on 146 ADPKD patients with pancreatic cysts labeled independently by two radiologists. Model performance was evaluated on test subjects unseen in training, comprising 40 internal, 40 external, and 23 test–retest reproducibility ADPKD patients. Results: Two radiologists agreed on 52% of cysts labeled on training data, and 33%/25% on internal/external test datasets. The 2D model with a loss of combined dice similarity coefficient and cross-entropy trained with the dataset with both positive and negative cases produced an optimal dice score of 0.7 ± 0.5/0.8 ± 0.4 at the voxel level on internal/external validation and was thus used as the best-performing model. In the test–retest, the optimal model showed superior reproducibility (83% agreement between scan A and B) in segmenting pancreatic cysts compared to six expert observers (77% agreement). In the internal/external validation, the optimal model showed high specificity of 94%/100% but limited sensitivity of 20%/24%. Conclusions: Labeling pancreatic cysts on T2 images of the abdomen in patients with ADPKD is challenging, deep learning can help the automated detection of pancreatic cysts, and further image quality improvement is warranted. Full article